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ASME Press Select Proceedings

International Symposium on Information Engineering and Electronic Commerce, 3rd (IEEC 2011)

Editor
C. B. Povloviq
C. B. Povloviq
National Technical University of Ukraine
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C. W. Lu
C. W. Lu
Huangshi Institute of Technology
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ISBN:
9780791859759
No. of Pages:
562
Publisher:
ASME Press
Publication date:
2011

Outlier detection has a wide range of applications, such as electronic commerce, credit card fraud detection and terrorism prediction which is related to the national security. Due to the importance of security and privacy, the study of distributed data mining is even more useful. Previous privacy-preserving outlier detection protocols over both horizontally and vertically partitioned data fail to deal with the case when the data is arbitrarily distributed among multi-party, and also leak information about the participants' privacy data. In this paper, we present a privacy-preserving distance-based outlier detection protocol over arbitrarily partitioned data without any information leakage. The protocol can also be applied to k-nearest neighbors mining. The security of our protocol is proved based on Secure Multi-party Computation theory.

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